Example #1
0
The possible number of clusters is in the [1,10] range
The proposed algorithm correctly selects a solution with 2 or 3 classes

Author : Bertrand Thirion, 2008-2009
"""
print __doc__

import numpy as np

import nipy.neurospin.clustering.gmm as gmm


dim = 2
# 1. generate a 3-components mixture
x1 = np.random.randn(100,dim)
x2 = 3+  2*np.random.randn(50,dim)
x3 = np.repeat(np.array([-2, 2], ndmin=2), 30, 0) \
     + 0.5*np.random.randn(30, dim)
x = np.concatenate((x1, x2, x3))

# 2. fit the mixture with a bunch of possible models
krange = range(1,5)
lgmm = gmm.best_fitting_GMM(x, krange,
                            prec_type='diag',
                            niter=100, delta=1.e-4,
                            ninit=1, verbose=0)

# 3, plot the result
z = lgmm.map_label(x)
gmm.plot2D(x, lgmm, z, show=1, verbose=0)
The possible number of clusters is in the [1,10] range
The proposed algorithm correctly selects a solution with 2 or 3 classes

Author : Bertrand Thirion, 2008-2009
"""
print __doc__

import numpy as np

import nipy.neurospin.clustering.gmm as gmm


dim = 2
# 1. generate a 3-components mixture
x1 = np.random.randn(100,dim)
x2 = 3+  2*np.random.randn(50,dim)
x3 = np.repeat(np.array([-2, 2], ndmin=2), 30, 0) \
     + 0.5*np.random.randn(30, dim)
x = np.concatenate((x1, x2, x3))

# 2. fit the mixture with a bunch of possible models
krange = range(1,5)
lgmm = gmm.best_fitting_GMM(x, krange, prec_type='diag', niter=100,
                            delta=1.e-4, ninit=1, verbose=0)

# 3, plot the result
z = lgmm.map_label(x)
gmm.plot2D(x, lgmm, z, verbose=0)
import pylab
pylab.show()
for  k in krange:
    b = bgmm.VBGMM(k, dim)
    b.guess_priors(x)
    b.initialize(x)
    b.estimate(x)
    ek = float(b.evidence(x))
    if ek>be:
        be = ek
        bestb = b
        
    print k,'classes, free energy:',ek

################################################################################
# 3. plot the result
z = bestb.map_label(x)
plot2D(x, bestb, z, verbose=0)
pl.title('Variational Bayes')

################################################################################
# 4. the same, with the Gibbs GMM algo
niter = 1000
krange = range(2, 6)
bbf = -np.infty
for k in krange:
    b = bgmm.BGMM(k, dim)
    b.guess_priors(x)
    b.initialize(x)
    b.sample(x, 100)
    w, cent, prec, pz = b.sample(x, niter=niter, mem=1)
    bplugin =  bgmm.BGMM(k, dim, cent, prec, w)
    bplugin.guess_priors(x)
for  k in krange:
    b = bgmm.VBGMM(k, dim)
    b.guess_priors(x)
    b.initialize(x)
    b.estimate(x)
    ek = float(b.evidence(x))
    if ek>be:
        be = ek
        bestb = b
        
    print k,'classes, free energy:',ek

################################################################################
# 3. plot the result
z = bestb.map_label(x)
plot2D(x, bestb, z, verbose=0)
pl.title('Variational Bayes')

################################################################################
# 4. the same, with the Gibbs GMM algo
niter = 1000
krange = range(2, 5)
bbf = -np.infty
for k in krange:
    b = bgmm.BGMM(k, dim)
    b.guess_priors(x)
    b.initialize(x)
    b.sample(x, 100)
    w, cent, prec, pz = b.sample(x, niter=niter, mem=1)
    bplugin =  bgmm.BGMM(k, dim, cent, prec, w)
    bplugin.guess_priors(x)